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A Systematic Review on Literature-based Discovery

Authors :
Thushari Atapattu
Katrina Falkner
Menasha Thilakaratne
Source :
ACM Computing Surveys. 52:1-34
Publication Year :
2019
Publisher :
Association for Computing Machinery (ACM), 2019.

Abstract

The vast nature of scientific publications brings out the importance of Literature-Based Discovery (LBD) research that is highly beneficial to accelerate knowledge acquisition and the research development process. LBD is a knowledge discovery workflow that automatically detects significant, implicit knowledge associations hidden in fragmented knowledge areas by analysing existing scientific literature. Therefore, the LBD output not only assists in formulating scientifically sensible, novel research hypotheses but also encourages the development of cross-disciplinary research. In this systematic review, we provide an in-depth analysis of the computational techniques used in the LBD process using a novel, up-to-date, and detailed classification. Moreover, we also summarise the key milestones of the discipline through a timeline of topics. To provide a general overview of the discipline, the review outlines LBD validation checks, major LBD tools, application areas, domains, and generalisability of LBD methodologies. We also outline the insights gathered through our statistical analysis that capture the trends in LBD literature. To conclude, we discuss the prevailing research deficiencies in the discipline by highlighting the challenges and opportunities of future LBD research.

Details

ISSN :
15577341 and 03600300
Volume :
52
Database :
OpenAIRE
Journal :
ACM Computing Surveys
Accession number :
edsair.doi...........25e17d9a6ed9cf8eb17df2c702b0c576
Full Text :
https://doi.org/10.1145/3365756